docstr: Generate a batch of reflection matrices of shape (batch_size, dim, dim),
  where M_i is an identity matrix with one random entry on the
  diagonal equal to -1.
docstr_tok: ["Generate","a","batch","of","reflection","matrices","of","shape","(","batch_size",",","dim",",","dim",")",",","where","M_i","is","an","identity","matrix","with","one","random","entry","on","the","diagonal","equal","to","-1","."]
code: def generate_random_reflection(batch_size=10, dim=3, device=None):
      """
      Generate a batch of reflection matrices of shape (batch_size, dim, dim),
      where M_i is an identity matrix with one random entry on the
      diagonal equal to -1.
      """
      # randomly select one of the dimensions to reflect for each
      # element in the batch
      dim_to_reflect = torch.randint(
          low=0,
          high=dim,
          size=(batch_size,),
          device=device,
          dtype=torch.int64,
      )
  
      # convert dim_to_reflect to a batch of reflection matrices M
      M = torch.diag_embed(
          (
              dim_to_reflect[:, None]
              != torch.arange(dim, device=device, dtype=torch.float32)
          ).float()
          * 2
          - 1,
          dim1=1,
          dim2=2,
      )
  
      return M
  
imports: import unittest
  import numpy
  import torch
  from pytorch3d.ops import points_alignment
  from pytorch3d.structures.pointclouds import Pointclouds
  from pytorch3d.transforms import rotation_conversions
  from .common_testing import get_tests_dir, TestCaseMixin
label_desc: Use image node_id 5 for calling the TestCorrespondingPointsAlignment obj's underlying member method code with example usage: obj.generate_random_reflection(batch_size, dim, device) and returns: M